基于非负矩阵分解新的人脸识别方法

Novel Methods of Face Recognition Based on Non-negative Matrix Factorization

非负矩阵分解是一个新的特征提取方法,基于非矩阵分解的理论,提出了具有正交性的投影轴的计算方法和具有统计不相关性的投影轴的计算方法。与原非负矩阵分解方法,提出的方法在某种程度上是降低了特征矢量之间的统计相关性,并且提高识别率。通过在ORL人脸库和YALE人脸库上进行实验,结果表明提出的两种特征提取方法在识别率方面整体上好于原非负矩阵分解特征提取(NMF)方法,甚至超过主成分分析(PCA)法。

Non-negative matrix factorization （NMF） is a new feature extraction method. Based on the Non-negative matrix factorization （NMF）, a new algorithm of orthogonal projection axis and a new algorithm of statistically uncorrelated projection axis for feature extraction were proposed, Compared with original NMF method, the proposed methods are better in terms of reducing or eliminating the statistical correlation between features and improving recognition rate. The experimental results on Olivetti Research Laboratory （ORL） face database and YALE face database show that the new methods are better than original NMF in terms of recognition rate and even outperform PCA.